Abstract
This article presents a conceptual model that explores the extent to which various (non)-users can be subjected to mechanisms of inclusion or exclusion. The model consists of eight profiles of digital inequalities, ranging from deep exclusion to deep inclusion, and is based upon a combination of five key indicators at the social level (income, education, social participation, agency, well-being) and eight key indicators at the digital level (access, attitudes, digital skills, soft skills, media richness of the environment, autonomy of use, user practices and social support). This conceptual model, by going further than socio-demographics, (a) allows the formulation of an alternative lens through which to look at mechanisms of inclusion and exclusion and (b) brings a significant contribution to existing research by highlighting the co-action of social and digital indicators in mechanisms of inclusion and exclusion.
Keywords
Introduction
Since it became clear that the Internet was to play a significant role in all aspects of life, general concerns about inequalities related to information and communications technologies (ICTs) have grown in policy and academic circles. Digital exclusion became a steady point in this debate in the late 1990s on the observation that access to and use of technologies were not distributed equally. Consequently, significant portions of the population were excluded from the opportunities provided by the Internet. It was then common to conceptualize these differences as a divide between those with access to ICTs and those without (DiMaggio et al., 2004). Over the past decades, the study of digital exclusion has shifted from the traditional dichotomy – access versus no access – to a recognition that exclusion is a complex and multifaceted phenomenon (Van Dijk, 2005, 2012) determined by factors such as social support networks (Asmar et al., 2020a), participation in society (Mariën and Baelden, 2015) or the role of life stages (Faure et al., 2020). Yet, it remains no longer clear how all these elements relate to one another and influence – in a positive or negative way – a meaningful inclusion in the digital society. Moreover, despite the fact that digital exclusion is recognized as a multifaceted phenomenon, it is still too often correlated with social exclusion, assuming too readily that low social and/or economic capital automatically supposes low digital inclusion.
We argue that such an approach is problematic for two reasons: (a) Exclusion is presented as a one-way street: once in situation of deprivation, users will keep on being pushed further out on the edges of society. Meanwhile, the inclusion of groups or individuals living in advantageous conditions seems to be taken for granted (Asmar et al., 2020b; Levitas et al., 2007; Mariën et al., 2016) and (b) by perceiving exclusion and vulnerability as a matter of demographics, such an approach fails to critically discuss the structural and situational factors that render specific individuals and groups more vulnerable to exclusion (Castel, 1995; Fineman, 2017). To be clear, we are not dismissing the importance of socio-demographics when looking at mechanisms of inclusion and exclusion. Rather, we advocate for a more nuanced approach with regard to the different factors – besides income or education – having a strong influence on the said mechanisms. Ultimately, exclusion is not merely a matter of identity or socio-demographics; instead, it is primarily a failure of society and its institutions to integrate individuals into the social entities through which they lead their lives (Byrne, 2005; Fineman, 2017).
Therefore, this article presents a renewed conceptual model that explores the extent to which various (non)-users can be subjected to mechanisms of inclusion and exclusion. The research questions behind the model are simple: (a) what additional factors, besides socio-demographic characteristics, determine the risks of digital exclusion and (b) how do these factors relate to and influence each other? The model consists of eight profiles of digital inequalities ranging on a continuum from deep exclusion to deep inclusion. The model is based on a combination of five key indicators at the social level (income, education, social participation, well-being, agency), and eight key indicators at the digital level (access, attitudes, digital skills, soft skills, autonomy of use, media character of the environment, user practices, social support).
Our model provides a significant contribution to existing research as (a) it highlights the co-action of social and digital factors. By going further than a sole focus on socio-demographic indicators, this conceptual model provides an alternative lens through which to look at mechanisms of inclusion and exclusion; (b) it is a call to action for civil society organizations and policymakers to help build customized digital inclusion strategies in light of the needs of each profile. The model can serve as a tool for civil society organizations and policymakers to (a) help and support those already living in vulnerable conditions; (b) detect – in a proactive manner – the indicators susceptible of putting people at risk of exclusion in the long run. The model rests on several research projects focused, on one hand, on the theoretical and methodological issues regarding the conceptualization and measurement of digital exclusion; and, on the other hand, on addressing the various barriers faced by people living in vulnerable situations when it comes to access to and use of digital technologies.
The article is structured as follows: in section ‘Digital exclusion: a vicious circle?’ we examine how the link between social and digital exclusion has been conceptualized within digital inequalities studies; in section ‘Being at-risk in the digital society: a clear-cut picture?’, we discuss the limitations of current conceptualizations and their implications for individuals and groups living in vulnerable conditions; in sections ‘No one-size-fits-all: three research components’ and ‘No one-size-fits-all: eight profiles of digital inequalities’, we develop our conceptual model and present the eight profiles of digital inequalities; in section ‘Discussion and conclusion’ we discuss the theoretical and societal implications of our model.
Digital exclusion: a vicious circle?
The digital divide has been traditionally conceptualized as the gap between individuals, households, and geographic areas at different socio-economic levels (Van Dijk, 2020). This gap refers both to their opportunities to access ICTs and their actual use of Internet for a wide array of activities (Hüsing and Selhofer, 2004). This approach documents the spread of the Internet across the population and has focused on the differences between those who have access to the Internet and those who do not (Hargittai and Hinnant, 2008; Van Dijk, 2020). However, as more people started using the Internet, research showed that the mere attention to binary classification was not helpful when discussing access to technology. Rather, more attention ought to be paid to the differences in how those who are online access and use technologies (Atwell, 2001; DiMaggio et al., 2004; Reisdorf and Groselj, 2017; Selwyn, 2004; Van Dijk, 2020; Warschauer, 2003). As such, Hargittai (2003) coins the term ‘digital inequalities’ to better encompass the different dimensions along which differences persist even after material access has been provided.
Yet, the term digital inequalities also prompted research to look closely at the divides among social groups with the argument that individuals or social groups excluded from using ICTs will automatically be excluded from the benefits that ICTs can bring (Bol et al., 2018; Selwyn, 2004). As such, the debate on inequalities started to delve further into understanding how the socio-economic status of individuals yields different effects with regard to access to and use of the Internet (Van Deursen et al., 2021; Van Dijk, 2020; Zillien and Hargittai, 2009). The term digital exclusion hence came to define ‘the lack of access to ICTs and the lack of skills needed to use them’ (Punie et al., 2009: 97). This specific form of exclusion came to be seen, on one hand, as a result of existing forms of social exclusion, and on the other hand, as a factor likely to aggravate other dimensions of social exclusion (Brants and Frissen, 2003; Park, 2017).
According to digital inequalities scholars (Haddon, 2000; Selwyn, 2006; Van Dijk, 2005; Witte and Mannon, 2010; Zillien and Hargittai, 2009), the unequal use of and access to the Internet tends to reproduce existing social divisions in terms of gender, race or even class. Henceforth, users run the risk of being excluded by factors outside their control such as material (i.e. income) or cultural resources, that is, the social assets (e.g. taste, education) that promote social mobility (Levitas et al., 2007; Warren, 2007). Scholars of digital inequalities point out that as social exclusion reinforces digital exclusion, so does digital exclusion, thus creating a vicious circle (Helsper, 2012; Warren, 2007). This suggests that groups already in situation of social exclusion could suffer further marginalization if unable to use and/or access digital technologies (Bol et al., 2018; Warren, 2007; Witte and Mannon, 2010). This resonates further with the fact that, as key aspects and services of everyday life now happen online (Townsend et al., 2020), high levels of access and skills become necessary to function fully in society as a citizen. Put differently, linking social exclusion to digital exclusion puts an emphasis on how the non-access and/or non-use of digital technologies could deny segments of the population membership in society (Helsper and Reisdorf, 2017; Reisdorf and Rhinesmith, 2020; Room, 1995). Such a perspective suggests that once mechanisms of exclusion have occurred in one area of life, they are likely to accumulate in other life areas (Helsper, 2012). Therefore, digital inequalities studies strongly focus on strategies of inclusion of vulnerable and socially excluded groups (Gilbert, 2010; Salemink, 2016; Selwyn, 2004).
However, most research on digital inequalities only offers snapshots of how digital and social exclusion are related (Goedhart et al., 2019). As a result, the link between social and digital exclusion continues lacking sufficient theorizations (Helsper, 2012). Although conceptual models exist, the root causes of mechanisms of inclusion and exclusion are still too easily reduced to socio-demographic factors such as education or income. Before presenting our model we outline the limitations of current theorizations and highlight why a more nuanced approach is needed.
Being at risk in the digital society: a clear-cut picture?
Since digital exclusion is closely related to social exclusion, socio-demographic factors play a great role in defining those at risk of digital exclusion. Segments of the population that are most likely to be excluded are usually defined in terms of age, gender, ethnicity, education or income (Levitas et al., 2007). Several digital inequalities scholars (Atwell, 2001; DiMaggio et al., 2004; Van Dijk, 2005, 2012) argue that individuals who have access to ICTs tend to have higher education and higher status occupation than those who lack access. Furthermore, low education, disability or age are said to reduce the likelihood of high-level access or skills (Van Deursen, 2018; Van Deursen and Van Dijk, 2010; Van Dijk, 2005).
While not dismissing the results of the aforementioned studies, we argue against discussions on digital exclusion that solely focuses on socio-demographic factors for their conceptualizations. In what follows, we outline the two limitations that such an approach presents.
Inclusion–exclusion: a one-way street?
Prevailing conceptualizations tend to see inclusion–exclusion as a one-way street: once in situation of deprivation, users will keep on being deprived. The reverse is equally implicit: once in situation of abundance, users will keep on taking advantage of their resources. This conceptualization has tremendous implications as to how being included and excluded is understood and researched. Within this view, the ‘rich’ – those with the resources at the social and/or digital level – are discursively absorbed in the ‘included’ majority, while the ‘poor’ – those without resources – seem to automatically fall back into the excluded minority. In doing so, such an approach draws attention away from the differences among the ‘included’ that potentially put them at risk of becoming digitally excluded. Recent research (Asmar et al., 2020a; Faure et al., 2020; Mariën and Baelden, 2015) shows that some ‘rich’ individuals (i.e. with high income), due to biographical ruptures (i.e. retirement), can refuse to engage with the digital and become potentially at risk of exclusion. Looking closely at marginalized groups such as Roma 1 travellers, Salemink (2016) and Townsend et al. (2020) show that current conceptualizations of digital exclusion mechanisms are not always appropriate for such groups. Salemink’s research (2016) shows that social exclusion does not necessarily preclude vulnerable groups such as Roma travellers from taking advantage of the digital. Socially excluded groups can become digitally engaged and develop their digital skills despite their social precarity.
Inclusion–exclusion: going further than demographics?
By addressing mechanisms of inclusion and exclusion from the sole perspective of socio-demographics, exclusion becomes the condition of a few – The poor, The lower educated – instead of being recognized as a structural form of inequality (Gilbert, 2010; Levitas, 2005; Salemink, 2016). While not denying the importance of socio-demographics factors, we rather contend that this focus tends to overemphasize states of deprivation (e.g. lack of access), rather than (a) looking into the processes and factors that trigger such states and (b) examining how different processes and factors influence one another in the generation of these states. An overemphasis on states of deprivation, without equal attention to the processes leading to such states, can be counter-effectual for the strategies designed to alleviate those at risk of exclusion. As pointed out by Mariën and Prodnik (2014), the positive impact of digital inclusion on social inclusion strategies can be limited by the fact that inequalities continue to be reproduced at a wider social level, feeding again into mechanisms of exclusion. In the same vein, Castel (1995) warns that exclusion is too often used as a catch-all term to cover a variety of situations but ignores the structural processes fostering exclusion in the first place.
Looking specifically at vulnerable groups, assigning the term ‘vulnerable’ only to certain groups helps perpetuate the myth that the digital and social inclusion of those outside this category is a given (Fineman, 2017). Put differently, perceiving vulnerability as determined by socio-economic factors forgoes the structural and situational factors that render specific groups or individuals vulnerable. Therefore, we refute Coekelbergh’s assertion (2013) according to which we are born unequal when it comes to vulnerability. We argue instead that vulnerability is universal (Fineman, 2017): we all share the same propensity to be wounded. However, the resources allocated to mitigate such propensity to harm are unevenly distributed. This results in situations where the vulnerability of some segments of the population is rendered more visible than that of others. Understanding how this unequal distribution of resources unevenly affects segments of the population requires developing approaches that address the distinct institutions and relationships that play a role in the reproduction of such inequalities.
No one-size-fits-all: three research components
Our model proposes an answer to the limitations outlined above in the following way. First, instead of pitting exclusion against inclusion, we put both concepts on an equal footing. Inspired by the works of Livingstone and Helsper (2007) and Castel (1995), we develop a continuum ranging from deep exclusion to deep inclusion. The continuum takes a graduated approach and captures the dynamic nature of mechanisms of inclusion and exclusion. This graduated approach is highlighted by the fluidity of the eight profiles. Indeed, far from being set in stone, life circumstances (e.g. birth of a child) or biographic ruptures (e.g. death of a spouse) can alter – positively or negatively – the positions of individuals on the continuum. Equally, the removal of obstacles (e.g. lack of access) can also allow individuals to move upward on the continuum, towards profiles characterized by deep inclusion. This liquid nature of the profiles further answers the limitations of prevailing conceptualizations posing inclusion–exclusion as a one-way street. Second, we go further than socio-demographics factors by combining social and digital indicators in the model. This model allows the development of customized digital inclusion strategies based on the detection of the most prominent indicators. Once identified, these prominent indicators (e.g. social support) can then be directly addressed and their impacts on the social and digital exclusion of individuals can be observed. In turn, the situations and contexts in which individuals experience difficulties can be recognized and acted upon through the implementation of customized strategies.
The eight profiles and the indicators on which they are based integrate, on one hand, theoretical insights from different academic perspectives, ranging from communication studies (Asmar et al., 2020b; Mariën and Prodnik, 2014; Zillien and Hargittai, 2009) to political sciences (Jehoel-Gijsbers and Vrooman, 2007); on the other hand, this model is the product of 7 years (2008–2015) of empirical study conducted through various research projects in the field of digital inclusion in Flanders (Belgium). These research projects build on the findings of each other to culminate in the elaboration of our model. Table 1 describes three core research projects in the elaboration of the eight profiles proposed.
Three core projects in the elaboration of the eight profiles.
In what follows we first present the three research components at the heart of the model. We then introduce the eight profiles of digital inequalities which are based on the results of the three research components.
Research component 1: a classification of the characteristics of social and digital inequalities
The first research component comprises a classification of the different determinants of social and digital exclusion. This classification, inspired by the work of Bourdieu (1986) and Helsper (2012), is divided across five types of resources that individuals have at their disposal and which we define as follows (Table 2).
Five types of resources.
Per type of resource we identify several risk factors associated with social and digital exclusion. These risk factors are subsequently thematically clustered (Tables 3 to 7). The emphasis lies on identifying the factors that put individuals in a disadvantaged position. This thematic clustering has been realized based on an extensive literature review spanning several disciplines – social inequalities, communication sciences (Van Dijk, 2005; Zillien and Hargittai, 2009), sociology (Gilbert, 2010), political sciences (Jehoel-Gijsbers and Vrooman, 2007).
Personal resources.
Social resources.
Sources: Crang et al. (2007), Gilbert (2010), Haché and Cullen (2010), Hargittai (2003), Jehoel-Gijsbers and Vrooman (2007), Van Dijk (2003), Warren (2007), Witte and Mannon (2010), and Zillien and Hargittai (2009).
Cultural resources.
Sources: Brants and Frissen (2003), Haché and Cullen (2010), Hargittai and Hinnant (2008), Jehoel-Gijsbers and Vrooman (2007), Van Dijk (2003) and Warren (2007).
Economic resources.
Sources: Brants and Frissen (2003), Daly et al. (2008), Gilbert (2010), Haché and Cullen (2010), Hargittai and Hinnant (2008), Jehoel-Gijsbers and Vrooman (2007), Livingstone and Helsper (2007), Van Dijk (2003, 2005), Warren (2007), Witte and Mannon (2010) and Zillien and Hargittai (2009).
Political resources.
Sources: Haché and Cullen (2010), Jehoel-Gijsbers and Vrooman (2007), Selwyn (2004), Van Dijk (2003, 2005) and Warren (2007).
Table 3 presents the clustering of risk factors associated with the personal resources.
Table 4 presents the clustering of risk factors associated with the social resources.
Table 5 presents the clustering of risk factors associated with the cultural resources.
Table 6 presents the clustering of risks factors associated with the economic resources.
Table 7 presents the clustering of risks factors associated with the political resources.
Research component 2: on the relation between social and digital exclusion
The second research component goes deeper into the concrete conceptualization of mechanisms of social and digital exclusion. In the first research component, we developed an extensive overview of the factors having a decisive influence within each type of resources. In this second research component, we map the dynamic interplay of these factors across the five resources explored in the first research component using the following colours:
Blue: the risk factors of social and digital exclusion such as psychological/physical well-being (personal resources), regulatory frameworks (political resources) have an indirect influence on each other. In other words, there is not a direct cause–effect relation between different risk factors, but an indirect impact on the social or digital situation.
Black: the impact of the risk factors such as gender (cultural resources) or family composition (social resources) is mostly felt at the level of social exclusion. The impact at the digital level depends on the interwovenness with other social and/or digital risk factors.
Green: mechanisms of social exclusion related to risk factors such as social/soft skills (personal resources) or support networks (social resources) directly lead to digital exclusion.
Red: mechanisms of digital exclusion related to risk factors such as employment (economic resources) or age (cultural resources) directly lead to social exclusion.
Delving deeper into these dynamics, and based on the risk factors identified in research component 1, we delineate two types of relations between social and digital exclusion: indirect and direct relations. The empirical and theoretical bases of the model allow us to further identify and select the most important risk factors associated with each of these relations.
Indirect relations refer to these risk factors that, while having an influence on both social and digital exclusion, manifest themselves differently within each field. They generally relate to policy-related risk factors. Combining theoretical insights (Mariën and Prodnik, 2014; Van Dijk, 2005) with empirical research (Table 1), we outline four main risk factors at play at both social and digital levels: (a) impact of regulatory frameworks, (b) public service infrastructures, (c) social rights and (d) well-being. To illustrate how these four factors operate at both levels yet with distinct characteristics, we take the example of the risk factor of well-being. Low levels of well-being at the social level are characterized, among others, by a lack of availability, access to care and support infrastructures; it refers also to the quality of care provided to individuals who are subject to substance abuse or are chronically ill (Jehoel-Gijsbers and Vrooman, 2007; Mariën et al., 2016; Warren, 2007). At the digital level, well-being often refers to a lack of usability standards, or a lack of available and affordable adaptive technologies (Mariën et al., 2016). Although at both levels well-being as a risk factor points out the specific needs of individuals dependent on the care of others, there is no direct cause–effect relation. This means that the lack of care at the social level, for instance, is not a direct cause to the lack of available adaptive technologies. However, there exists an indirect relation. For example, a better regulation of adaptive technologies – at the local level through national policies, but also globally through common international guidelines – could significantly enhance the care and support infrastructures at the social level; this would provide solutions to the direct ICT-barriers that prevent individuals with special needs to fully participate in society.
Direct relations between social and digital exclusion are twofold: on one hand, social exclusion mechanisms are transferred to the digital field; on the other hand, digital exclusion mechanisms reinforce existing processes of social exclusion.
First, looking at the direct relations where mechanisms of social exclusion are transferred or exacerbated at the digital level, we identify the following risk factors:
Personal resources: cognitive and social skills,
Economic resources: employment, income;
Social resources: social support networks;
Political resources: socio-spatial inequalities, agency, participation;
Cultural resources: language, education.
We posit a direct relationship insofar as, at the social as well as the digital level, these risk factors present the same characteristics; put differently, there is a direct transfer or cause–effect relation from one field to the other. For example, low levels of self-esteem, low communication skills impede interactions in daily life but also with ICTs. Such limitations are often linked to what Van Dijk (2005) calls ‘button anxiety’ or the fear that arises when having to deal with ICTs. However, there exist exceptions nuancing this claim. For some people with low self-esteem and/or communication skills, an online environment can provide a safe space for social interactions. In such case, the relation between social and digital exclusion becomes an indirect one with risk factors taking different forms at the social and digital levels.
Second, in direct relations where mechanisms of digital exclusion reinforce social exclusion, we identify the following risk factors:
Personal resources: values, attitudes, norms;
Economic resources: financial skills, economic opportunities;
Social resources: societal norms;
Political resources: regulatory frameworks, public services infrastructures;
Cultural resources: age, gender.
This second aspect relates specifically to the rapid and ongoing digitalization of public and private services. The shift towards the ‘digital-by-default’ society (Yates et al., 2015) is increasingly making it mandatory for individuals to use ICTs. This in turn limits individuals’ ability to make free choices and leads to increasing user disempowerment (Crang et al., 2007; Mariën and Prodnik, 2014). Those who do not want to, or are unable to use ICTs are progressively excluded from societal services such as education or employment (Mariën and Prodnik, 2014; Yates et al., 2015).
Research component 3: towards a continuum from deep exclusion to deep inclusion
The third research component comprises a reworking of the classification of Miliband (2006). As shown in Table 8, the original classification of Miliband (2006) comprises three levels, namely (a) wide social exclusion, (b) concentrated social exclusion and (c) deep social exclusion.
A classification of social exclusion by Miliband (2006).
The second research component showed that the relationship between social and digital inequalities is not always straightforward: socially excluded groups can be digitally included, and socially included groups can equally be digitally excluded. To provide a comprehensive view of mechanisms of inclusion and exclusion it is necessary to analyse social and digital inequalities across a full continuum from deep exclusion to deep inclusion. Therefore, we expand below the classification of Miliband (2006) to five levels related to both social and digital fields (Table 9).
Continuum from deep exclusion to deep inclusion.
No one-size-fits-all: eight profiles of digital inequalities
The three research components above have provided different insights with respect to the understanding of mechanisms of inclusion and exclusion. However, such exercise still does not make clear which individuals or segments of the population are those most at risk of exclusion. In order to curtail such complexity, we develop profiles of the groups or individuals at risk of exclusion based on the three research components discussed in the previous section. These profiles are defined as follows: (a) Digital Outcasts, (b) Hopelessly Undigital, (c) Digital Fighters, (d) Smoothly Digital, (e) Digital All-Stars, (f) Unexpected Digital Master, (g) Digital Drop-Outs and (h) Digitally Self-Excluded. The development of these profiles follows three phases: first, based on the findings of the three research components, we were able to identify and select the most important indicators having a decisive influence on mechanisms of inclusion and exclusion. Yet, this selection still yielded a wide range of factors, rendering thus impractical any attempts at a profiling exercise. Therefore, combining the results of the three research components with insights gathered through additional research (Asmar et al., 2020b; Mariën, 2016; Mariën et al., 2019), we were able to narrow down the selection and identify five indicators at the social level and eight indicators at the digital level (Table 10).
Thirteen at-risk indicators.
Second, based on results of the first research component, the impact of each indicator is identified for each level of the continuum (research component 2), giving, for example, the following output (Table 11).
Indicators in deep social and digital exclusion.
Finally, the characteristics of each indicator are tested against the dynamic interplay of factors of social and digital exclusion mapped out in the second research component (Figure 1). From this, eight profiles of digital inequalities emerge.

Dynamics of social and digital exclusion.
The name of each profile has been chosen with governments and civil society organizations in mind. For such institutions, easily identifiable profiles are important to comprehend the complex processes at work when trying to (a) identify individuals’ needs and (b) implement strategies to alleviate these needs. First, the profiles have been tested with civil society organizations (e.g. community centres), who, based on their hands-on experience, were able to confirm the accuracy of these profiles and their descriptions. Second, the profiles have been tested and discussed with citizens in Belgium and the Netherlands during research projects and presentations at scientific fairs (Mariën et al., 2019). There too, the descriptions of the profiles almost always matched the perceptions and feelings of the citizens interviewed. Moreover, the names used for each profile, far from being negatively perceived, triggered curiosity and helped open discussions regarding the use of and access to digital media. Finally, the profiles have been used with Belgian policymakers who acknowledged the usefulness of such a tool in helping to understand the complexity of mechanisms of inclusion–exclusion (Table 12).
Eight profiles of digital inequalities: an overview, by Mariën and Baelden (2015).
Sources: Mariën and Prodnik (2014).
The five profiles on the left of the continuum epitomize the direct relations between social and digital exclusion. For the Digital Outcasts, Hopelessly Undigital and Digital Fighters, mechanisms of social exclusion lead to digital exclusion. Individuals in these profiles are confronted with a multitude of social barriers that are not only strongly intertwined, but exacerbated in the digital field. In contrast, for the Smoothly Digital and the Digital All-Stars, their successes at the digital level are a tribute to their social situation. Both profiles are socially included and able to reap the benefits of this inclusion at the digital level.
The final three profiles on the right side of the continuum (Unexpected Digital Masters, Unexpected Digital Drop-Outs; Digitally Self-Excluded) show an indirect relation between social and digital exclusion. These three profiles come from various socio-economic groups (i.e. elderly and highly educated) and, contrary to the profiles on the left of the continuum, their social and/or digital (dis)advantages are not connected to their social/digital situations.
In what follows, we present each of these eight profiles in detail.
Profile 1: Digital Outcasts
Individuals in this profile are in situations of deep social and digital exclusion. They are confronted with multiple difficulties at the social level (i.e. low income and unemployment) and at the digital level (i.e. low quality equipment and lack of autonomy). These social and digital barriers perpetuate and reinforce each other continuously. They are strongly intertwined and cannot be bridged by the individual alone. Without support, individuals in this profile are slowly being pushed to the edges of society and made increasingly vulnerable to exclusion.
Profile 2: Hopelessly Undigital
Individuals in this profile face wide social exclusion, that is to say, they experience multiple obstacles impeding their participation in society. They try to keep up with the constant digital evolutions but their social situation makes it difficult for them to succeed. Moreover, their societal participation is very limited. While they can occasionally use digital media, they seldom succeed in doing so in an autonomous manner. Besides, increasing and diversifying their media use is complicated by the fact that they lack opportunities and social support in their environment. They rarely use digital media for job-related purposes and live mostly in a media-poor environment. They experience the use of digital media as an obligation and have the feeling that they are increasingly becoming socially excluded because of the far-reaching digitalization of society.
Profile 3: Digital Fighters
Individuals in this profile are in a situation of concentrated social exclusion; this means that they are socially included in several life domains but remain excluded from others. For example, women who are socially, culturally and politically included, but who are economically excluded because of a limited participation in the job market. With regard to digital media, we distinguish two large groups:
(a) Individuals who experience wide digital exclusion and thus face multiple barriers such as lack of skills, self-confidence or access. These individuals have the motivation to use digital media. Nevertheless, keeping up with the digital evolutions is a constant struggle as they lack the necessary competences and support to follow the rapid innovations in an autonomous way.
(b) Individuals who experience concentrated digital exclusion and thus face very specific barriers. For instance, they can lack digital skills despite the good quality of their equipment and the motivation to use technology. Dealing with digital media remains difficult because they have to rely strongly on their support networks. As such, they are at risk of slipping into Profile 2, hopelessly undigital.
Profile 4: Smoothly Digital
Individuals in this profile experience wide social and digital inclusion. Their use of digital media is guided by their daily needs; therefore, this profile comprises both heavy and light users. They have different usage patterns depending on what they want or need to achieve. Individuals in this profile do what they can to stay up-to-date with the digital evolutions but will mainly use digital media in a very functional manner. For example, they will acquire a smartphone only to send e-mails, or play video games. They do not question the digitalization of society and display generally low motivation to use technology. They trust their ability to communicate clearly with others and have overall good problem-solving skills. However, they still need a bit of time to learn new skills and can be at risk of exclusion once their social support networks disappear or if their living conditions change (i.e. job loss). Yet, these individuals also succeed in continually strengthening the way they deal with digital media because of the various resources and learning opportunities in their environment. They can function as a source of support for other users in their environment.
Profile 5: Digital All-Stars
Individuals in this profile are deeply socially and digitally embedded. They participate in all life domains and experience no obstacles in their use of digital media. They use all kinds of technologies in an autonomous, strategic and creative manner. They learn by doing (i.e. trial and error) and, being often highly educated with high incomes, they have access to digital media anytime, anywhere. They form the social support networks for other users in their environment, but do not necessarily enjoy taking on this role, as it can be time-consuming and without added value for them. They take part in all forms of technological innovations and have the skills necessary to challenge these systems.
Profile 6: Unexpected Digital Masters
Individuals in this profile are generally in situations of social exclusion (i.e. people in poverty, unemployed). The determining factor is that, in contrast to their peers, they fully engage with digital media. In other words, their socio-economic and cultural backgrounds have little to no negative influence on their digital engagement. They may be confronted to one or two very specific barriers at the digital level – that is, lack of home access – but manage to bypass these difficulties by falling back on their social networks and/or making use of public computer rooms. They display high motivation to use digital media and to learn by doing. They continuously experiment with technology, and develop their skills in an autonomous way. They very often provide support to their peers with less digital skills.
Profile 7: Unexpected Digital Drop-Outs
These individuals are generally well included socially (i.e. middle class families, highly educated). The determining factor for these individuals is that, in contrast to their peers, they experience various problems when using digital media. In other words, their socio-economic and cultural backgrounds have little to no positive influence on their digital media use. Despite the presence of support networks in their media-rich environment, individuals in this profile generally avoid dealing with digital media; they do not manage to develop their digital skills and usually display low motivation to engage with digital technologies. Besides, the lack of self-confidence and soft skills constitutes an important barrier for this group.
Profile 8: Digitally Self-Excluded
Individuals in this profile come from all socio-economic backgrounds, regardless of the degree of social or digital exclusion. They participate in all life domains and are generally satisfied with their living conditions. What sets this group apart is that individuals in this profile generally have access to digital media and possess the necessary skills to use technology, but they do not see the utility of using digital media. Individuals in this profile make extensive use of proxy users to use digital media or access services only available digitally; yet, once their social network disappears, they can rapidly find themselves in vulnerable situations.
In analysing social and digital inequalities across a continuum ranging from deep exclusion to deep inclusion, the conceptual model of eight profiles proposed here highlights that the relationship between social and digital inequalities is not a one-way street. Rather, the positions of individuals along the continuum are complex and subject to change. Life events (e.g. birth of children), lifestyles, family turmoil and so on, can alter or influence one’s profile in a positive or negative direction. Empirical testing and application of this model (Mariën et al., 2019) has shown, for example, that an individual is not forever bound to be ‘Hopelessly Undigital’: individuals in this profile usually have the motivation to learn to use digital technologies. With the right support, they generally succeed in moving towards profiles characterized by wide and/or deep social and digital inclusion (e.g. smoothly digital). The reverse is equally true: advantaged profiles (e.g. digital all-stars) can regress in their digital autonomy (e.g. losing motivation to learn and losing opportunity to access technologies). Furthermore, this model demonstrates the fluid and dynamic nature of mechanisms of inclusion and exclusion. Individuals can fit into more than just one profile. Our empirical research (Mariën et al., 2019) shows that some individuals can find themselves in-between two profiles, that is to say, the characteristics of two profiles – generally very close to one another on the continuum – apply to them. The graduated approach to mechanisms of inclusion and exclusion taken by the model serves thus to highlight the co-action of digital and social factors, and to show inclusion and exclusion as a process – the product of a host of situations and factors – rather than a state of being.
Discussion and conclusion
In this last section, we explore the theoretical and societal implications of our model. At a theoretical level, this conceptual model leads to two important observations. First, it shows that the extent to which socio-demographic factors lead to digital exclusion is not straightforward. Digital exclusion is determined by a host of additional factors such as soft skills or social support. The model highlights (a) how the social vulnerability of some individuals (i.e. Digital Outcasts) impede their digital inclusion, yet it also demonstrates; (b) how socially excluded individuals (i.e. Unexpected Digital Masters), despite their social precarity, can manage full inclusion in the digital society. This confirms earlier results of Salemink (2016) and Townsend et al. (2020). Moreover, the model makes clear that digital vulnerability and exclusion also constitute real risks for individuals with high economic or cultural capital (i.e. Digitally Self-Excluded). Indeed, by focusing on additional factors – besides income or education – this model sheds light on the overarching conditions and structures that render individuals and groups vulnerable to social and digital exclusion.
Second, the eight profiles contribute to existing research by (a) identifying indicators that have a critical influence on mechanisms of inclusion–exclusion and (b) highlighting their co-action at the digital and social level. By critically reflecting on the extent to which (non)-users can be subjected to mechanisms of inclusion–exclusion, the eight profiles demonstrate the dynamic interplay of different factors in the generation of these mechanisms. This interplay shows that, while socio-demographics continue to play a role, the extent to which they lead to digital exclusion is influenced by additional factors. With millions of people around the globe migrating online in response to enforced lockdowns, our ongoing research on the impact of COVID-19 in Flanders (Belgium) points at the importance of social support as crucial indicator of mechanisms of exclusion. In fact, our preliminary results (Van Audenhove et al., 2020) show that the profiles situated at the left of the continuum – Digital Outcasts, Hopelessly Undigital, Digital Fighters – were the most affected by this online migration. Before the pandemic, these three profiles were already experiencing various challenges at the social and digital levels. Yet, as the world moved online to work, socialize and learn, these profiles appear to have been severely impacted. Without access to quality equipment, digital skills training or support when experiencing technical difficulties, individuals in these profiles have been left out of virtually every sphere of life. Further than mere technical support, we are starting to see the important role played by the informal support provided by family, friends and/or colleagues. Not only do they bring emotional reassurance during the learning process (Asmar et al., 2020a), but they are also the source of support that, before social distancing measures got in place, was almost always readily at hand. The isolation generated by these measures affected not only the acquisition and development of digital skills, but also the emotional and mental well-being of the eight profiles at large, and those at the left of the continuum in particular.
At a societal level and in light of preliminary results (Wauters et al., 2020), this conceptual model stresses the importance of developing community-based inclusion strategies that give a key role to civil society organizations. Being digitally included is too often perceived as a matter of individual responsibility (Asmar et al., 2020a; Mariën and Prodnik, 2014). Yet, the continued importance of social support during the pandemic shows that digital inclusion is an eminently social matter. Therefore, we argue that rethinking inclusion strategies means involving civil society organizations (e.g. community centres and youth groups). Actively involved in matters of digital and social inclusion, these organizations act as facilitators between society and various social groups. Furthermore, we hypothesize that in the aftermath of COVID-19, civil society organizations will be at the frontline to provide support and assistance to populations and individuals who experienced the most severe difficulties during the pandemic-related lockdowns. At the policy level, involving these organizations means that structural funding is needed to allow them to keep functioning even in times of crisis. However, looking specifically at Flanders (Belgium), we observe that a lot is done for the provision of needs – that is, providing access to computer rooms – but not enough is done to prevent such needs from occurring in the first place. Presently, because of uncertain and limited funding, civil society organizations can only provide ad hoc solutions to situations of digital exclusion. They remain unable to address them upstream. As explained by Castel (1995), exclusion is the end of a process: there is a need to understand how the specific situations and/or contexts of individuals can result in exclusion and how to prevent new groups/individuals from falling in said situations. If nothing is done upstream, that is to say if the situations and contexts are not taken into account, fighting exclusion will just result in playing the role of an emergency rapid respond unit: minimizing the tear in the social tissue but without ever developing clear and defined long-term strategies (Castel, 1995). Understanding and responding to situations of digital exclusion necessitates thus the development of personalized strategies adapted to the experiences of each and every one.
Footnotes
Funding
The author(s) received no financial support for the research, authorship and/or publication of this article.
